A Hierarchical Neural Network for Sleep Stage Classification Based on Comprehensive Feature Learning and Multi-Flow Sequence Learning

• Published in: IEEE journal of biomedical and health informatics 2020-05, Vol.24 (5), p.1351-1366
• Main Authors: Sun, Chenglu, Chen, Chen, Li, Wei, Fan, Jiahao, Chen, Wei
• Format: Article
• Language: English
• Subjects: Ablation; Adolescent; Adult; Brain modeling; Classification; Convolutional Neural Network; EEG Signal; Electrodiagnosis; Electroencephalography; Electromyography; Electrooculography; Feature extraction; Female; Humans; Learning; Machine Learning; Male; Middle Aged; Neural networks; Neural Networks, Computer; Physiology; Polysomnography - methods; Recurrent Neural Network; Recurrent neural networks; Signal processing; Signal Processing, Computer-Assisted; Sleep; Sleep Stage Classification; Sleep Stages - physiology; Young Adult
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2019.2937558

Automatic sleep staging methods usually extract hand-crafted features or network trained features from signals recorded by polysomnography (PSG), and then estimate the stages by various classifiers. In this study, we propose a classification approach based on a hierarchical neural network to process multi-channel PSG signals for improving the performance of automatic five-class sleep staging. The proposed hierarchical network contains two stages: comprehensive feature learning stage and sequence learning stage. The first stage is used to obtain the feature matrix by fusing the hand-crafted features and network trained features. A multi-flow recurrent neural network (RNN) as the second stage is utilized to fully learn temporal information between sleep epochs and fine-tune the parameters in the first stage. The proposed model was evaluated by 147 full night recordings in a public sleep database, the Montreal Archive of Sleep Studies (MASS). The proposed approach can achieve the overall accuracy of 0.878, and the F1-score is 0.818. The results show that the approach can achieve better performance compared to the state-of-the-art methods. Ablation experiment and model analysis proved the effectiveness of different components of the proposed model. The proposed approach allows automatic sleep stage classification by multi-channel PSG signals with different criteria standards, signal characteristics, and epoch divisions, and it has the potential to exploit sleep information comprehensively.